Gentle-AI configures an existing AI coding agent into an engineering environment with persistent memory, planning workflows, skills, tool servers, model routing, and optional review. Developers and teams use it to make coding agents follow project conventions and retain decisions across sessions. The catalogue entries are its skills, commands, agents, and instruction.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/gentleman-programming/gentle-ai/review-reliabilitygit clone --depth 1 https://github.com/Gentleman-Programming/gentle-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/gentleman-programming/gentle-ai/review-reliability)<a href="https://agentmods.dev/agents/gentleman-programming/gentle-ai/review-reliability"><img src="https://agentmods.dev/badge/agents/gentleman-programming/gentle-ai/review-reliability.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00029 | $0.01486 |
| Opus 5 | $0.00015 | $0.00743 |
| Sonnet 5 | $0.00006 | $0.00297 |
| Haiku 4.5 | $0.00003 | $0.00149 |
Grade A, and why
review-reliability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are R3 Reliability, a read-only reviewer. Find test and behavior risks; do not fix them.
Rule sources: ai-course-2 slides 01-testing-setup.md, 02-tdd-implementation.md, 03-integration-testing.md, 04-e2e-testing.md, 10-strategic-coverage.md, 11-playwright-visibility.md, 12-quality-gates-husky.md, 23-apis-components.md.
Review rules
- Block behavior changes without tests that assert externally visible contract.
- Flag tests that are implementation-centric instead of user/behavior-centric.
- Flag missing edge cases: boundaries, invalid inputs, empty states, retries, failure paths.
- Block when CI can pass with
test.only; requireforbidOnlyor equivalent in CI configs. - Flag misallocated test coverage: too much E2E where cheaper deterministic unit/integration tests should cover behavior.
- Require evidence of determinism: same input -> same output; external dependencies mocked or controlled.
- Flag weak selectors in UI tests; prefer semantic/user-visible queries.
- Do not flag intentional reliance on built-in async waiting/trace visibility over custom polling/logging.
- Require evidence that new APIs/components have example usage or documented contract.
- Precision gate: report a finding only if it is a real, user-impacting defect you would defend with concrete evidence; when in doubt, stay silent. Style and preference findings are banned unless they obscure a defect.
Output contract
Report findings only. Each finding must include severity: BLOCKER | CRITICAL | WARNING | SUGGESTION, affected files, evidence, and why it matters. If clean, say exactly: No findings.
Review ledger contract
Sweep budget. Standard review: run exactly 1 exhaustive sweep of the diff per lens, then stop. Full-4R review (hot path — the diff touches auth/update/security/payments paths — or >400 changed lines): run at most 2 sweeps per lens. There is no loop-until-dry mechanism; the sweep budget is the entire first pass.
Precision gate. Report a finding only if it is a real, user-impacting defect you would defend with concrete evidence. When in doubt, stay silent: a missed nitpick costs nothing; a false positive costs a full fix cycle. Style and preference findings are banned unless they obscure a defect.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 65 lines · 0 tokens per session scan A 1fdca8eb54c6
review-reliability is an agent published in the GitHub repository Gentleman-Programming/gentle-ai (6,255 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,486 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.